1. Field
The disclosed concept pertains generally to electric loads and, more particularly, to methods of identifying different types of electric loads. The disclosed concept also pertains to systems for identifying different types of electric loads.
2. Background Information
Electric loads in commercial and residential buildings consumed about 75% of total electricity in the U.S. in 2012. However, a large portion of this electricity use has been wasted, and the management of this usage has often been overlooked. Many electric appliances with an external power supply, a remote control, a continuous display or a battery charger continuously draw power in an off or standby mode. Electric loads with external power supplies are also called plugged-in electric loads (PELs) (or miscellaneous electric loads in some contexts). PELs is one of the major load categories and accounts for more use than any other end-use service, such as heating and ventilation.
Standby power in the U.S. accounts for over 100 billion kWh and costs over $10 billion annually. As much as 75% of this cost can be saved by proper energy management. In order to achieve the Net-Zero-Energy-Building goals defined by the Department of Energy (DOE) for residential buildings by 2020 and for commercial buildings by 2025, the effective monitoring and management of PELs needs to be considered. Knowing the type of PELs is essential to enabling an effective solution.
Since the introduction of non-intrusive load monitoring (NILM) in the 1980s, numerous prior proposals have sought to develop various NILM solutions. A wide-range of known solutions is disclosed by Du et al., “A review of identification and monitoring methods for electric loads in commercial and residential buildings,” Proc. 2010 IEEE Energy Conversion Conf. and Expo., 2010, pp. 4527-33.
A load identification system typically consists of several modules including data acquisition, data processing, event detection, feature extraction, and identity indication. The identity indication module compares the extracted features with a database of features of known loads and identifies unknown loads based on pre-defined rules, such as maximum similarity or learning results of artificial neural networks (ANNs).
The performance of almost all existing load identification methods highly depends on the electrical signatures of loads, which are defined to be “an electrical expression that a load device or appliance distinctly possesses”. The objective is to extract useful features that can uniquely distinguish the individual PELs types or classes within a predetermined load set.
Many feature extraction methods have been proposed. For example, for steady state feature exploration, real and reactive power is utilized to identify load types. Also, peak current, average current and RMS current values can be used for load identification. Current harmonics are applied as the core features for identification to mainly address those loads with a nonlinear power supply. Further, a voltage-current (V-I) trajectory modeling method for load identification uses purely graphical shape features of the V-I trajectory of each load. Also, some transient state features, such as instantaneous admittance curves and transient power curves, can be employed.
The development of feature extraction and the assignment of each load type to a corresponding load group and sub-group has been purely data-driven. Even though many prior proposals demonstrate that satisfactory performance can be achieved by selecting a proper set of features for a targeted load set, there are no known guidelines to drive an optimized feature selection, and there is potentially a redundancy of information in any set of features. Moreover, the identification performance usually depends on the specific load set under study. It is believed that how well the performance of the developed classifier can be generalized to other load sets has not yet been addressed, and that there does not exist a set of electrical signatures such that every load can have a “distinct” expression.
Due to the complexity and nuances of devices and appliances, it is often challenging, if not impossible, to distinguish between loads that use the same interface circuit to a power line. For example, those PELs using a standardized direct current (DC) power supply with current harmonic reduction, such as DVD players, cable or satellite set-top boxes, and PC monitors, present very similar electrical signatures, and are not distinguishable by only using the steady state features. Hence, a truly meaningful load categorization method is often still desired.
There is room for improvement in methods of identifying different electric load types.
There is also room for improvement in systems for identifying different electric load types.